# Correlation is significant at the 005 level 2 tailed

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*. Correlation is significant at the 0.05 level (2-tailed). Interpretation : Cells indicate perfect correlations (“1”) because these cells reflect the correlation of a variable with itself. Pearson Correlation: These numbers measure the strength and direction of the linear relationship between the two variables. The correlation coefficient can range from -1 to +1, with -1 indicating a perfect negative correlation, +1 indicating a perfect positive correlation, and 0 4

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indicating no correlation at all. (A variable correlated with itself will always have a correlation coefficient of 1.) You can think of the correlation coefficient as telling you the extent to which you can guess the value of one variable given a value of the other variable. From the scatterplot of the variables read and write below, we can see that the points tend along a line going from the bottom left to the upper right, which is the same as saying that the correlation is positive. The . 597 is the numerical description of how tightly around the imaginary line the points lie. If the correlation was higher, the points would tend to be closer to the line; if it was smaller, they would tend to be further away from the line. Also note that, by definition, any variable correlated with itself has a correlation of 1. Sig. (2-tailed) : This is the p-value associated with the correlation. The footnote under the correlation table explains what the single and double asterisks signify. We will reject Ho if the p-value is above .05, if the p-value is not below the .05 so we retain the Ho N : This is number of cases that was used in the correlation. Because we have no missing data in this data set, all correlations were based on all 200 cases in the data set. However, if some variables had missing values, the N's would be different for the different correlations. Regression: Linear regression is Model Summary Model R R Square Adjusted R Square Std. Error of the Estimate 1 .464 a .215 .164 2.65637 a. Predictors: (Constant), CSR, CB, LI, PR, TR, CR 4
Coefficients a Model Unstandardized Coefficients Standardized Coefficients t Sig. B Std. Error Beta 1 (Constant) 9.648 2.425 3.979 .000 CB .421 .104 .410 4.045 .000 PR .353 .219 .176 1.614 .110 TR -.056 .314 -.023 -.178 .859 CR .002 .169 .001 .010 .992 LI .017 .215 .009 .081 .936 CSR -.046 .127 -.047 -.364 .716 a. Dependent Variable: AD Interpretation: Model Summary: gives you the r value, the r 2 value Coefficients: gives you (a, b) values, and the p-value to check for significance. We reject Ho if p≤ .05. This means the relationship is reliable and can be used to make predictions Y’= bx + a Consumer buying behavior = 9.648+ 0.42(CB) + 0.353 (PR) – 0.056 (TR) + 0.002 (CR) + 0.017 (LI) – 0.046 (LI) 4

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CHAPTER NO. 7 CONCLUSION For the image building of the product and better market share the advertiser should have to keep the following things in mind. First of all the advertisements should be based on variety and reality. There should be more emphasis on quality rather than glamour and price. While to make an advertisement effective it should be telecasted only 3 or 4 times in a day, otherwise it creates boredom. The advertisers should have to keep this thing in mind that an advertisement conveys
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